class tf.contrib.keras.optimizers.Nadam
Defined in tensorflow/contrib/keras/python/keras/optimizers.py.
Nesterov Adam optimizer.
Much like Adam is essentially RMSprop with momentum, Nadam is Adam RMSprop with Nesterov momentum.
Default parameters follow those provided in the paper. It is recommended to leave the parameters of this optimizer at their default values.
Arguments:
lr: float >= 0. Learning rate.
beta_1/beta_2: floats, 0 < beta < 1. Generally close to 1.
epsilon: float >= 0. Fuzz factor.
References: - Nadam report - On the importance of initialization and momentum in deep learning
Methods
__init__
__init__(
lr=0.002,
beta_1=0.9,
beta_2=0.999,
epsilon=1e-08,
schedule_decay=0.004,
**kwargs
)
from_config
from_config(
cls,
config
)
get_config
get_config()
get_gradients
get_gradients(
loss,
params
)
get_updates
get_updates(
params,
constraints,
loss
)
get_weights
get_weights()
Returns the current value of the weights of the optimizer.
Returns:
A list of numpy arrays.
set_weights
set_weights(weights)
Sets the weights of the optimizer, from Numpy arrays.
Should only be called after computing the gradients (otherwise the optimizer has no weights).
Arguments:
weights: a list of Numpy arrays. The number
of arrays and their shape must match
number of the dimensions of the weights
of the optimizer (i.e. it should match the
output of `get_weights`).
Raises:
ValueError: in case of incompatible weight shapes.